Mobile Vertical Ranking based on Preference Graphs

We consider the problem of ranking relevant verticals for
a given
mobile search query so as to satisfy the average user. To this end,
we utilise real mobile search click logs, and apply a graph contruction
algorithm proposed by Agrawal et al. who tackled the problem
of automatically assigning relevance labels to URLs for general
web search. While Agrawal et al. ordered URLs based on pairwise
preferences and then partitioned the ordered URL list to determine
absolute relevance grades, our objective is to rank a given set of
verticals for a given query, to help search engine companies select
which verticals to include in a search engine result page for a small
smartphone screen. We show that “Click Skip Other” preference
rules consistently outperform more conservative rules such
as “Click Skip Previous,” and that our best graph-based vertical
ranking methods substantially and statistically significantly outperform
a competitive baseline that ranks verticals based on click
counts.